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An important question in neuroscience is whether and how temporal patterns and fluctuations in neuronal spike trains contribute to information processing in the cortex. We have addressed this issue in the memory-related circuits of the prefrontal cortex by analyzing spike trains from a database of 229 neurons recorded in the dorsolateral prefrontal cortex of 4 macaque monkeys during the performance of an oculomotor delayed-response task. For each task epoch, we have estimated their power spectrum together with interspike interval histograms and autocorrelograms. We find that 1) the properties of most (about 60%) neurons approximated the characteristics of a Poisson process. For about 25% of cells, with characteristics typical of interneurons, the power spectrum showed a trough at low frequencies (<20 Hz) and the autocorrelogram a dip near zero time lag. About 15% of neurons had a peak at <20 Hz in the power spectrum, associated with the burstiness of the spike train, 2) a small but significant task dependency of spike-train temporal structure: delay responses to preferred locations were characterized not only by elevated firing, but also by suppressed power at low (<20 Hz) frequencies, and 3) the variability of interspike intervals is typically higher during the mnemonic delay period than during the fixation period, regardless of the remembered cue. The high irregularity of neural persistent activity during the delay period is likely to be a characteristic signature of recurrent prefrontal network dynamics underlying working memory.

Working memory capacity, the maximum number of items that we can transiently store in working memory, is a good predictor of our general cognitive abilities. Neural activity in both dorsolateral prefrontal cortex and posterior parietal cortex has been associated with memory retention during visuospatial working memory tasks. The parietal cortex is thought to store the memories. However, the role of the dorsolateral prefrontal cortex, a top-down control area, during pure information retention is debated, and the mechanisms regulating capacity are unknown. Here, we propose that a major role of the dorsolateral prefrontal cortex in working memory is to boost parietal memory capacity. Furthermore, we formulate the boosting mechanism computationally in a biophysical cortical microcircuit model and derive a simple, explicit mathematical formula relating memory capacity to prefrontal and parietal model parameters. For physiologically realistic parameter values, lateral inhibition in the parietal cortex limits mnemonic capacity to a maximum of 2-7 items. However, at high loads inhibition can be counteracted by excitatory prefrontal input, thus boosting parietal capacity. Predictions from the model were confirmed in an fMRI study. Our results show that although memories are stored in the parietal cortex, interindividual differences in memory capacity are partly determined by the strength of prefrontal top-down control. The model provides a mechanistic framework for understanding top-down control of working memory and specifies two different contributions of prefrontal and parietal cortex to working memory capacity.

Cognitive functions, including working memory capacity, improve during childhood and early adulthood. Several maturational processes take place during that time, most importantly the myelination of axons, pruning of synapses and strengthening of the remaining synapses. However, it has not yet been shown how to directly relate these cellular changes to working memory development and associated changes in brain activity. Here, we bridge this gap by integrating biophysically-based computational modelling and functional MRI of the visuospatial working memory. Cellular mechanisms corresponding to different maturational processes were implemented in in silico 'child' networks, and the predicted difference in activity between 'child' and a reference 'adult' network was then compared to measured brain activity in children and adults. Network models with stronger connectivity between brain areas, but not networks with faster conduction or increased neuronal specificity, were supported by measured developmental increases in brain activity and correlations between frontal and parietal areas. The 'adult' networks with stronger fronto-parietal connections also exhibited greater stability during distraction, which was consistent with the developmental improvement in working memory performance.

Recently, important insights into static network topology for biological systems have been obtained, but still global dynamical network properties determining stability and system responsiveness have not been accessible for analysis. Herein, we explore a genome-wide gene-to-gene regulatory network based on expression data from the cell cycle in Saccharomyces cerevisae (budding yeast). We recover static properties like hubs (genes having several out-going connections), network motifs and modules, which have previously been derived from multiple data sources such as whole-genome expression measurements, literature mining, protein-protein and transcription factor binding data. Further, our analysis uncovers some novel dynamical design principles; hubs are both repressed and repressors, and the intra-modular dynamics are either strongly activating or repressing whereas inter-modular couplings are weak. Finally, taking advantage of the inferred strength and direction of all interactions, we perform a global dynamical systems analysis of the network. Our inferred dynamics of hubs, motifs and modules produce a more stable network than what is expected given randomised versions. The main contribution of the repressed hubs is to increase system stability, while higher order dynamic effects (e.g. module dynamics) mainly increase system flexibility. Altogether, the presence of hubs, motifs and modules induce few flexible modes, to which the network is extra sensitive to an external signal. We believe that our approach, and the inferred biological mode of strong flexibility and stability, will also apply to other cellular networks and adaptive systems.

The quest to determine cause from effect is often referred to as reverse engineering in the context of cellular networks. Here we propose and evaluate an algorithm for reverse engineering a gene regulatory network from time-series kind steady-state data. Our algorithmic pipeline, which is rather standard in its parts but not in its integrative composition, combines ordinary differential equations, parameter estimations by least angle regression, and cross-validation procedures for determining the in-degrees and selection of nonlinear transfer functions. The result of the algorithm is a complete directed net-work, in which each edge has been assigned a score front it bootstrap procedure. To evaluate the performance, we submitted the outcome of the algorithm to the reverse engineering assessment competition DREAM2, where we used the data corresponding to the InSillico1 and InSilico2 networks as input. Our algorithm outperformed all other algorithms when inferring one of the directed gene-to-gene networks.

Using deep sequencing (deepCAGE), the FANTOM4 study measured the genome-wide dynamics of transcription-start-site usage in the human monocytic cell line THP-1 throughout a time course of growth arrest and differentiation. Modeling the expression dynamics in terms of predicted cis-regulatory sites, we identified the key transcription regulators, their time-dependent activities and target genes. Systematic siRNA knockdown of 52 transcription factors confirmed the roles of individual factors in the regulatory network. Our results indicate that cellular states are constrained by complex networks involving both positive and negative regulatory interactions among substantial numbers of transcription factors and that no single transcription factor is both necessary and sufficient to drive the differentiation process.

Most diseases are caused by a mixture of environmental and genetic components. The genetic component is mainly inherited but can also induced by the environment. Cancer and cardiovascular diseases are not affected by a single gene but more often by a number of genes a nd also the complex system that the interactions between these genes form. To understand and treat these complex diseases we need a better understanding of the underlying gene networks and what parts of the network the diseases target. In the first study presented here we show that text mining of the biological literature together with whole-genome expression data can be used to identify gene networks and that the resulting network edges can be ranked according to their biological reliability. In the second study we present a novel algorithm, CutTree, that can identify the genetic targets of compounds using only a small number of whole-genome expression experiments. Computational tools like these will facilitate the exploration of gene networks in health and disease.

Tegnér, Jesper

Abstract [en]

Background: Since biological networks are believed to govern the cellular behavior under normal and diseased conditions there is a large interest in developing methods that can identify the underlying structure of those networks There has been an explosion of studies using text mining to extract useful biological information from the published biomedical literature as accessed through PubMed. Co-occurrence of gene symbols in abstracts have been proposed as a method to reconstruct gene networks. On the other hand, rapid progress in micro-array technology have produced extensive data-sets of the activity of the entire genome under different biological conditions. Yet, it is not clear how to validate and assess the quality of these inferred networks beyond visual inspection and case studies and it is not feasible to reconstruct gene networks directly from whole genome wide expression data . Here we present a novel method which integrates prior knowledge in the form of published articles with whole-genome wide expression measurements.

Results: We have developed a benchmark system, using a Yeast gene network as a reference network. which enables us to determine the optimal parameters for how to integrate the information from both abstracts and full texts of published articles with whole genome wide expression data sets. We investigate how the quality of the network reconstruction depends on the number of articles used, whether only using abstracts as compared to full text articles. We develop a comprehensive network reconstruction algorithm that utilizes several criteria, including the frequency of co-occurrences in abstracts and full texts, to rank which edges that are most likely to be present in the network.

Conclusions: Our method is a practical tool to effectively identify as many reliable edges as possible in a gene network combining text mining and whole-genome expression data. Our scheme could easily be integrated with other methods and other data types, such as sequence information, in order to find putative interactions between genes.

Abstract [en]

Background

A key problem of drug development is to decide which compounds to evaluate further in expensive clinical trials (Phase I- III). This decision is primarily based on the primary targets and mechanisms of action of the chemical compounds under consideration. Whole-genome expression measurements have shown to be useful for this process but current approaches suffer from requiring either a large number of mutant experiments or a detailed understanding of the regulatory networks.

Results

We have designed an algorithm, CutTree that when applied to whole-genome expression datasets identifies the primary affected genes (PAGs) of a chemical compound by separating them from downstream, indirectly affected genes. Unlike previous methods requiring whole-genome deletion libraries or a complete map of gene network architecture, CutTree identifies PAGs from a limited set of experimental perturbations without requiring any prior information about the underlying pathways. The principle for CutTree is to iteratively filter out PAGs from other recurrently active genes (RAGs) that are not PAGs. The in silico validation predicted that CutTree should be able to identify 3–4 out of 5 known PAGs (~70%). In accordance, when we applied CutTree to whole-genome expression profiles from 17 genetic perturbations in the presence of galactose in Yeast, CutTree identified four out of five known primary galactose targets (80%). Using an exhaustive search strategy to detect these PAGs would not have been feasible (>1012 combinations).

Conclusion

In combination with genetic perturbation techniques like short interfering RNA (siRNA) followed by whole-genome expression measurements, CutTree sets the stage for compound target identification in less well-characterized but more disease-relevant mammalian cell systems.

A key problem of drug development is to decide which compounds to evaluate further in expensive clinical trials (Phase I- III). This decision is primarily based on the primary targets and mechanisms of action of the chemical compounds under consideration. Whole-genome expression measurements have shown to be useful for this process but current approaches suffer from requiring either a large number of mutant experiments or a detailed understanding of the regulatory networks.

Results

We have designed an algorithm, CutTree that when applied to whole-genome expression datasets identifies the primary affected genes (PAGs) of a chemical compound by separating them from downstream, indirectly affected genes. Unlike previous methods requiring whole-genome deletion libraries or a complete map of gene network architecture, CutTree identifies PAGs from a limited set of experimental perturbations without requiring any prior information about the underlying pathways. The principle for CutTree is to iteratively filter out PAGs from other recurrently active genes (RAGs) that are not PAGs. The in silico validation predicted that CutTree should be able to identify 3–4 out of 5 known PAGs (~70%). In accordance, when we applied CutTree to whole-genome expression profiles from 17 genetic perturbations in the presence of galactose in Yeast, CutTree identified four out of five known primary galactose targets (80%). Using an exhaustive search strategy to detect these PAGs would not have been feasible (>1012 combinations).

Conclusion

In combination with genetic perturbation techniques like short interfering RNA (siRNA) followed by whole-genome expression measurements, CutTree sets the stage for compound target identification in less well-characterized but more disease-relevant mammalian cell systems.

Slow Ca2+ oscillations caused by release from intracellular stores have been observed in neurons in the lamprey spinal cord. These oscillations are triggered by activation of metabotropic glutamate receptors on the cell surface. The pathway leading from receptor activation to the inositol triphosphate-mediated release of Ca2+ from the endoplasmatic reticulum has been modelled in order to facilitate further understanding of the nature of these oscillations. The model generates Ca2+ oscillations with a frequency range of 0.01–0.09 Hz. A prediction of the model is that the frequency will increase with a stronger extracellular glutamate signal.

Background: Since biological networks are believed to govern the cellular behavior under normal and diseased conditions there is a large interest in developing methods that can identify the underlying structure of those networks There has been an explosion of studies using text mining to extract useful biological information from the published biomedical literature as accessed through PubMed. Co-occurrence of gene symbols in abstracts have been proposed as a method to reconstruct gene networks. On the other hand, rapid progress in micro-array technology have produced extensive data-sets of the activity of the entire genome under different biological conditions. Yet, it is not clear how to validate and assess the quality of these inferred networks beyond visual inspection and case studies and it is not feasible to reconstruct gene networks directly from whole genome wide expression data . Here we present a novel method which integrates prior knowledge in the form of published articles with whole-genome wide expression measurements.

Results: We have developed a benchmark system, using a Yeast gene network as a reference network. which enables us to determine the optimal parameters for how to integrate the information from both abstracts and full texts of published articles with whole genome wide expression data sets. We investigate how the quality of the network reconstruction depends on the number of articles used, whether only using abstracts as compared to full text articles. We develop a comprehensive network reconstruction algorithm that utilizes several criteria, including the frequency of co-occurrences in abstracts and full texts, to rank which edges that are most likely to be present in the network.

Conclusions: Our method is a practical tool to effectively identify as many reliable edges as possible in a gene network combining text mining and whole-genome expression data. Our scheme could easily be integrated with other methods and other data types, such as sequence information, in order to find putative interactions between genes.

Atherosclerosis is the main underlying cause of coronary artery disease (CAD) and stroke. These two diseases are leading causes of death in the developed world. The socio-economical cost for treatments and absence of work is enormous. Recent numbers from the US show no tendency of decline in the spread of atherosclerosis.

Common risk factors for premature atherosclerosis are obesity, diabetes, smoking, physical inactivity and high blood pressure. Medications have been developed for treatment of risk factors and a breakthrough was the release of statins, an effective lipid-lowering drug. Nevertheless, atherosclerosis is multi-factorial and all the different players in the pathological process are not yet identified. New large-scale studies, such as gene expression profiling, are needed to understand the interplay between risk factors and pathways in atherosclerosis and with that reveal new therapeutic targets.

In this thesis two studies are presented where gene expression profiling in well-characterized patients suffering from severe atherosclerosis have been performed. In study number one, five types of tissues (atherosclerotic aortic root, unatherosclerotic mammary artery, mediastinal fat, skeletal muscle and liver) were collected from a total of 66 patients undergoing coronary by-pass surgery. Subjects were also screened for cardiovascular risk factors. RNA was isolated from the biopsies and gene expression analysis using Affymetrix GeneChip system was performed. The main result showed that mediastinal fat appears to be central in CAD.

In study number two, gene expression analysis of plaques from patients undergoing carotid endorectomy was performed. Again, all patients were screened for conventional risk factors and RNA was isolated and expression profiles were obtained using Affymetrix technology. Cluster analyses identified genes associated to intima-media thickness in these patients.

Taken together, expression analyses of clinical whole-genome expression datasets can be used to identify novel pathways and individual genes with possible importance for atherosclerosis development.

Lundström, Jesper

Noori, Peri

Skogsberg, Josefin

Nilsson, Roland

Brinne, Björn

Hallén, Kristofer

Silveira, Angela

Lockowandt, Ulf

Liska, Jan

Franco-Cereceda, Anders

Ivert, Torbjörn

Hamsten, Anders

Tegnér, Jesper

Björkegren, Johan

Show others...

(English)Manuscript (preprint) (Other academic)

Abstract [en]

BACKGROUND

By offering a comprehensive view of the molecular underpinnings of pathology, high-dimensional data have the potential to revolutionize the diagnosis and management of complex disorders such as coronary artery disease (CAD). To identify molecular phenotypes of CAD, we performed multi organ gene expression profiling of subjects enrolled in the Stockholm Atherosclerosis Gene Expression (STAGE) study.

The most prominent molecular phenotype of the CAD patients was represented by 733 genes in mediastinal fat, which were involved in extracellular matrix organization, response to stress and regulation of programmed cell death. Other aspects of this phenotype were shared with liver (e.g., oxidoreductase activity), skeletal muscle (insulin-like growth factor binding), and atherosclerotic arterial wall (cell motility and adhesion, fatty acid metabolism). In addition, the activity of 400 genes exclusively in mediastinal fat was associated with the extent of coronary stenosis and atherosclerosis. Immune-cell activation in mediastinal fat defined CAD patients with poor blood glucose control and prolonged hospitalization.

CONCLUSIONS

The molecular phenotype of mediastinal fat appears to be central in CAD and should be useful for early identification of CAD risk.

Atherosclerosis is a progressive inflammatory disease that causes lipid accumulation in the arterial wall, leading to the formation of plaques. The clinical manifestations of plaque rupture—stroke and myocardial infarction—are increasing worldwide and pose an enormous economic burden for society. Atherosclerosis development reflects a complex interaction between environmental exposures and genetic predisposition. To understand this complexity, we hypothesized that a top-down approach—one in which all molecular activities that drive atherosclerosis are examined simultaneously—is necessary to highlight those that are clinically relevant. To this end, we performed whole-genome expression profiling in multiple tissues isolated from patients with coronary artery disease (CAD).

In the Stockholm Atherosclerosis Gene Expression (STAGE) study, biopsies of five tissues (arterial wall with and without atherosclerotic lesions, liver, skeletal muscle and visceral fat) were isolated from 124 CAD patients undergoing coronary artery bypass grafting surgery (CABG) at the Karolinska University Hospital, Solna and carotid lesions from 39 patients undergoing carotid artery surgery at Stockholm Söder Hospital. Detailed clinical characteristics of these patients were assembled together with a total of 303 global gene expression profiles obtained with the Affymetrix GeneChip platform.

In paper 1, a two-way clustering analysis of the data identified 60 tissue clusters of functionally related genes. One cluster, partly present in both visceral fat and atherosclerotic lesions, related to atherosclerosis severity as judged by coronary angiograms. Many of the genes in that cluster were also present in a carotid lesion cluster relating to intima-media thickness (IMT) in the carotid patients. The union of all three clusters relating to extent of atherosclerosis—referred to as the “A-module”—was overrepresented with genes belonging to the transendothelial migration of leukocyte (TEML) pathway. The transcription co-factor, Lim domain binding 2 (LDB2), was identified as putative regulator of the A-module and TEML pathway in validation studies including Ldb2-/- mice.

In paper 2, we investigated the increased incidence of postoperative complications in CABG patients with diabetes. Using the STAGE compendium, we identified an anti-inflammatory marker, dual-specificity phosphatase 1 (DUSP1), as a novel preoperative blood marker of risk for a prolonged hospital stay after CABG.

In paper 3, plaque age was determined with C14-dating in the carotid patients. Interestingly, the strongest correlation with plaque age was not the age of the patients or IMT. Rather, the strongest correlations were with plasma insulin levels and inflammatory gene expression.

Taken together, the findings in this thesis show that a top-down approach using multi-tissue gene expression profiling in CAD and C14-dating of plaques can contribute to a better understanding of the molecular processes underlying atherosclerosis development and to the identification of clinically useful biomarkers.

Bradshaw, Maria

Bajic, Vladimir B.

South African National Bioinformatics Institute (SANBI), University of the Western Cape, Cape Town, South Africa, and Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia.

Abstract [en]

Environmental exposures filtered through the genetic make-up of each individual alter the transcriptional repertoire in organs central to metabolic homeostasis, thereby affecting arterial lipid accumulation, inflammation, and the development of coronary artery disease (CAD). The primary aim of the Stockholm Atherosclerosis Gene Expression (STAGE) study was to determine whether there are functionally associated genes (rather than individual genes) important for CAD development. To this end, two-way clustering was used on 278 transcriptional profiles of liver, skeletal muscle, and visceral fat (n=66/tissue) and atherosclerotic and unaffected arterial wall (n=40/tissue) isolated from CAD patients during coronary artery bypass surgery. The first step, across all mRNA signals (n=15,042/12,621 RefSeqs/genes) in each tissue, resulted in a total of 60 tissue clusters (n=3958 genes). In the second step (performed within tissue clusters), one atherosclerotic lesion (n=49/48) and one visceral fat (n=59) cluster segregated the patients into two groups that differed in the extent of coronary stenosis (P=0.008 and P=0.00015). The associations of these clusters with coronary atherosclerosis were validated by analyzing carotid atherosclerosis expression profiles. Remarkably, in one cluster (n=55/54) relating to carotid stenosis (P=0.04), 27 genes in the two clusters relating to coronary stenosis were confirmed (n=16/17, P<10-27and-30). Genes in the transendothelial migration of leukocytes (TEML) pathway were overrepresented in all three clusters, referred to as the atherosclerosis module (A-module). In a second validation step, using three independent cohorts, the A-module was found to be genetically enriched with CAD risk by 1.8-fold (P<0.004). The transcription co-factor LIM domain binding 2 (LDB2) was identified as a potential high-hierarchy regulator of the A-module, a notion supported by subnetwork analysis, cellular and lesion expression of LDB2, and the expression of 13 TEML genes in Ldb2-deficient arterial wall. Thus, the A-module appears to be important for atherosclerosis development and together with LDB2 merits further attention in CAD research.

Place, publisher, year, edition, pages

PLoS Genetics, 2009

National Category

Natural Sciences

Identifiers

urn:nbn:se:liu:diva-52084 (URN)10.1371/journal.pgen.1000754 (DOI)

Note

On the day of the defence day the status of this article was: In Press.Available from: 2009-12-03 Created: 2009-12-03 Last updated: 2009-12-07Bibliographically approved

Abstract [en]

Background: Identifying patients who are at increased risk of morbidity and prolonged post-operative stay is of interest from both health-economic and individual patient perspectives. Patients with diabetes often present with inflammatory conditions and have prolonged hospitalization after CABG. The recent development of technologies to generate high-dimensional data provides an opportunity to identify preoperative markers that can be used to help optimize preoperative planning to minimize postoperative complications.

Results: As shown in other studies, diabetic CABG patients in the STAGE cohort also had prolonged hospitalization time (P<0.02). Out of ~50 000 mRNAs measures in the liver, skeletal muscle and visceral fat in 66 STAGE patients, the mRNA levels of anti-inflammatory gene dual specificity phosphatase-1 (DUSP1) correlated independently with post-operative rehabilitation and separated the patients into those with normal (8 days) and prolonged hospitalization (>8 days). In the validation cohort, preoperative blood levels of DUSP1 separated patients with short and long hospitalization stay (P=9x10-10).

Conclusions: From genome scans in three separate organs, we identified the anti-inflammatory gene DUSP1 as a pre-operative marker indicating risk for prolonged postoperative stay after CABG.

Abstract [en]

Rationale: The exact nature of atherosclerotic plaque development and the molecular mechanisms that lead to clinical manifestations of carotid stenosis are unclear. After nuclear bomb tests in the 1950s, atmospheric 14C concentrations rapidly increased. Since then, the concentrations have been declining, and the curve of declination can be used to date biological samples synthesized during the last five to six decades.

Objective: To investigate plaque age as a novel characteristic of atherosclerotic plaques in patients with carotid stenosis.

Methods and Results: Carotid plaques from 29 well-characterized endarterectomy patients with symptomatic carotid stenosis were analyzed by accelerator mass spectrometry, and global gene expression of 25 plaque samples was profiled with HG-U133 Plus 2.0 arrays. The average plaque age was 9.3 years, and inter- and intrasample standard variations were low (1–3.5 years); thus, most of the plaques were generated 5–15 years before surgery. Plaque age was not associated with patient age or plaque size, determined by intima-media thickness, but was inversely related to plasma insulin levels (P=0.0014). A cluster of functionally related genes enriched with genes involved in immune responses was activated in plaques with low plaque age, as were oxidative phosphorylation genes.

Conclusion: Patients with mild insulin resistance have increased immune and inflammatory gene activity in their carotid plaques causing them to become instable, rapidly progressing into clinical manifestations at a relatively young age. These results show that plaque age, determined by 14C dating, is a novel and important characteristic of atherosclerotic plaques that will improve our understanding of the clinical significance and molecular underpinnings of atherosclerosis.

Background: Identifying patients who are at increased risk of morbidity and prolonged post-operative stay is of interest from both health-economic and individual patient perspectives. Patients with diabetes often present with inflammatory conditions and have prolonged hospitalization after CABG. The recent development of technologies to generate high-dimensional data provides an opportunity to identify preoperative markers that can be used to help optimize preoperative planning to minimize postoperative complications.

Results: As shown in other studies, diabetic CABG patients in the STAGE cohort also had prolonged hospitalization time (P<0.02). Out of ~50 000 mRNAs measures in the liver, skeletal muscle and visceral fat in 66 STAGE patients, the mRNA levels of anti-inflammatory gene dual specificity phosphatase-1 (DUSP1) correlated independently with post-operative rehabilitation and separated the patients into those with normal (8 days) and prolonged hospitalization (>8 days). In the validation cohort, preoperative blood levels of DUSP1 separated patients with short and long hospitalization stay (P=9x10-10).

Conclusions: From genome scans in three separate organs, we identified the anti-inflammatory gene DUSP1 as a pre-operative marker indicating risk for prolonged postoperative stay after CABG.

By offering a comprehensive view of the molecular underpinnings of pathology, high-dimensional data have the potential to revolutionize the diagnosis and management of complex disorders such as coronary artery disease (CAD). To identify molecular phenotypes of CAD, we performed multi organ gene expression profiling of subjects enrolled in the Stockholm Atherosclerosis Gene Expression (STAGE) study.

The most prominent molecular phenotype of the CAD patients was represented by 733 genes in mediastinal fat, which were involved in extracellular matrix organization, response to stress and regulation of programmed cell death. Other aspects of this phenotype were shared with liver (e.g., oxidoreductase activity), skeletal muscle (insulin-like growth factor binding), and atherosclerotic arterial wall (cell motility and adhesion, fatty acid metabolism). In addition, the activity of 400 genes exclusively in mediastinal fat was associated with the extent of coronary stenosis and atherosclerosis. Immune-cell activation in mediastinal fat defined CAD patients with poor blood glucose control and prolonged hospitalization.

CONCLUSIONS

The molecular phenotype of mediastinal fat appears to be central in CAD and should be useful for early identification of CAD risk.

Rationale: The exact nature of atherosclerotic plaque development and the molecular mechanisms that lead to clinical manifestations of carotid stenosis are unclear. After nuclear bomb tests in the 1950s, atmospheric 14C concentrations rapidly increased. Since then, the concentrations have been declining, and the curve of declination can be used to date biological samples synthesized during the last five to six decades.

Objective: To investigate plaque age as a novel characteristic of atherosclerotic plaques in patients with carotid stenosis.

Methods and Results: Carotid plaques from 29 well-characterized endarterectomy patients with symptomatic carotid stenosis were analyzed by accelerator mass spectrometry, and global gene expression of 25 plaque samples was profiled with HG-U133 Plus 2.0 arrays. The average plaque age was 9.3 years, and inter- and intrasample standard variations were low (1–3.5 years); thus, most of the plaques were generated 5–15 years before surgery. Plaque age was not associated with patient age or plaque size, determined by intima-media thickness, but was inversely related to plasma insulin levels (P=0.0014). A cluster of functionally related genes enriched with genes involved in immune responses was activated in plaques with low plaque age, as were oxidative phosphorylation genes.

Conclusion: Patients with mild insulin resistance have increased immune and inflammatory gene activity in their carotid plaques causing them to become instable, rapidly progressing into clinical manifestations at a relatively young age. These results show that plaque age, determined by 14C dating, is a novel and important characteristic of atherosclerotic plaques that will improve our understanding of the clinical significance and molecular underpinnings of atherosclerosis.

South African National Bioinformatics Institute (SANBI), University of the Western Cape, Cape Town, South Africa, and Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia.

Environmental exposures filtered through the genetic make-up of each individual alter the transcriptional repertoire in organs central to metabolic homeostasis, thereby affecting arterial lipid accumulation, inflammation, and the development of coronary artery disease (CAD). The primary aim of the Stockholm Atherosclerosis Gene Expression (STAGE) study was to determine whether there are functionally associated genes (rather than individual genes) important for CAD development. To this end, two-way clustering was used on 278 transcriptional profiles of liver, skeletal muscle, and visceral fat (n=66/tissue) and atherosclerotic and unaffected arterial wall (n=40/tissue) isolated from CAD patients during coronary artery bypass surgery. The first step, across all mRNA signals (n=15,042/12,621 RefSeqs/genes) in each tissue, resulted in a total of 60 tissue clusters (n=3958 genes). In the second step (performed within tissue clusters), one atherosclerotic lesion (n=49/48) and one visceral fat (n=59) cluster segregated the patients into two groups that differed in the extent of coronary stenosis (P=0.008 and P=0.00015). The associations of these clusters with coronary atherosclerosis were validated by analyzing carotid atherosclerosis expression profiles. Remarkably, in one cluster (n=55/54) relating to carotid stenosis (P=0.04), 27 genes in the two clusters relating to coronary stenosis were confirmed (n=16/17, P<10-27and-30). Genes in the transendothelial migration of leukocytes (TEML) pathway were overrepresented in all three clusters, referred to as the atherosclerosis module (A-module). In a second validation step, using three independent cohorts, the A-module was found to be genetically enriched with CAD risk by 1.8-fold (P<0.004). The transcription co-factor LIM domain binding 2 (LDB2) was identified as a potential high-hierarchy regulator of the A-module, a notion supported by subnetwork analysis, cellular and lesion expression of LDB2, and the expression of 13 TEML genes in Ldb2-deficient arterial wall. Thus, the A-module appears to be important for atherosclerosis development and together with LDB2 merits further attention in CAD research.

Recent experimental observations of spike-timing-dependent synaptic plasticity (STDP) have revitalized the study of synaptic learning rules. The most surprising aspect of these experiments lies in the observation that synapses activated shortly after the occurrence of a postsynaptic spike are weakened. Thus, synaptic plasticity is sensitive to the temporal ordering of pre- and postsynaptic activation. This temporal asymmetry has been suggested to underlie a range of learning tasks. In the first part of this review we highlight some of the common themes from a range of findings in the framework of predictive coding. As an example of how this principle can be used in a learning task, we discuss a recent model of cortical map formation. In the second part of the review, we point out some of the differences in STDP models and their functional consequences. We discuss how differences in the weight-dependence, the time-constants and the non-linear properties of learning rules give rise to distinct computational functions. In light of these computational issues raised, we review current experimental findings and suggest further experiments to resolve some controversies.

The performance on various cognitive tasks, from language to selective attention and guiding future actions depends on working memory (WM), the capacity to hold and manipulate limited items of information. However, neural basis of WM and the capacity limitation are still unclear. The present work includes behavioral, functional magnetic resonance imaging (fMRI) and computational modeling studies of the visuospatial WM in order to identify the neural correlates of WM capacity. In the first study we used behavioral distracting stimuli in order to identify cellular mechanisms that accounts for the observed behavioral decrease in mnemonic accuracy as a function of distractor distance. The study provided theoretical support that independently of the cellular and synaptic properties, increased neuronal firing rates accounted for higher mnemonic accuracy and resistance against distractors. In the second study we performed fMRI experiments on adults and children to monitor brain activity during a WM task. We isolated the delay-related activity and analyzed group differences and the distractor influence both behaviorally and in terms of changed brain activity. The fMRJ study showed higher brain activity in inferior frontal and intraparietal cortex in adults compared to children during the delay periods of WM tasks. Furthermore, adults were more accurate and less distractible than children. In a subsequent study we addressed the cellular changes during WM development. The study combined a computational analysis with fMRl in order to establish putative maturational processes governing developmental changes in brain activity. We found that the increase in activit' together with higher resistance against distractors could be explained by computational models having stronger connectivity between network areas. Our studies suggest that increased firing rates of the cortical areas involved in the maintenance of visuospatial information accounts for the developmental related increase of activity in areas associated with WM processes as well as for the higher resistance against distractors. Therefore, increasing the neural activity of the WM circuitry using either psychophysiological training protocols or pharmacological manipulation may have a beneficial effect on the WM capacity and distractibility.

Abstract [en]

Persistent neural activity constitutes one neuronal correlate of working memory, the ability to hold and manipulate information across time, a prerequisite for cognition. Yet, the underlying neuronal mechanisms are still elusive. Here, we design a visuo- spatial delayed-response task to identify the relationship between the cue-distractor spatial distance and mnemonic accuracy. Using a shared experimental and computational test protocol, we probe human subjects in computer experiments, and subsequently we evaluate different neural mechanisms underlying persistent activity using an in silico prefrontal network model. Five modes of action of the network were tested: weak or strong synaptic interactions, wide synaptic arborization, cellular bistability and reduced synaptic NMDA component. The five neural mechanisms and the human behavioral data, all exhibited a significant deterioration of the mnemonic accuracy with decreased spatial distance between the distractor and the cue. A subsequent computational analysis revealed that the firing rate and not the neural mechanism per se, accounted for the positive correlation between mnemonic accuracy and spatial distance. Moreover, the computational modeling predicts an inverse correlation between accuracy and distractibility. In conclusion, any pharmacological modulation, pathological condition or memory training paradigm targeting the underlying neural circuitry and altering the net population firing rate during the delay is predicted to determine the amount of influence of a visual distraction.

Abstract [en]

In order to retain information in working memory (WM) during a delay, distracting stimuli must be ignored. This important ability improves during childhood, but the neural basis for this development is not known. We measured brain activity with functional magnetic resonance imaging in adults and 13-year-old children. Data were analyzed with an event-related design to isolate activity during cue, delay, distraction, and response selection. Adults were more accurate and less distractible than children. Activity in the middle frontal gyrus and intraparietal cortex was stronger in adults than in children during the delay, when information was maintained in WM. Distraction during the delay evoked activation in parietal and occipital cortices in both adults and children. However, distraction activated frontal cortex only in children. The larger frontal activation in response to distracters presented during the delay may explain why children are more susceptible to interfering stimuli.

Klingberg, Torkel

Abstract [en]

Cognitive functions, including working memory capacity, improve during childhood and early adulthood. Several maturational processes take place during that time, most importantly the myelination of axons, pruning of synapses and strengthening of the remaining synapses. However, it has not yet been shown how to directly relate these cellular changes to working memory development and associated changes in brain activity. Here, we bridge this gap by integrating biophysically-based computational modelling and functional MRI of the visuospatial working memory. Cellular mechanisms corresponding to different maturational processes were implemented in in silico 'child' networks, and the predicted difference in activity between 'child' and a reference 'adult' network was then compared to measured brain activity in children and adults. Network models with stronger connectivity between brain areas, but not networks with faster conduction or increased neuronal specificity, were supported by measured developmental increases in brain activity and correlations between frontal and parietal areas. The 'adult' networks with stronger fronto-parietal connections also exhibited greater stability during distraction, which was consistent with the developmental improvement in working memory performance.

Persistent neural activity constitutes one neuronal correlate of working memory, the ability to hold and manipulate information across time, a prerequisite for cognition. Yet, the underlying neuronal mechanisms are still elusive. Here, we design a visuo- spatial delayed-response task to identify the relationship between the cue-distractor spatial distance and mnemonic accuracy. Using a shared experimental and computational test protocol, we probe human subjects in computer experiments, and subsequently we evaluate different neural mechanisms underlying persistent activity using an in silico prefrontal network model. Five modes of action of the network were tested: weak or strong synaptic interactions, wide synaptic arborization, cellular bistability and reduced synaptic NMDA component. The five neural mechanisms and the human behavioral data, all exhibited a significant deterioration of the mnemonic accuracy with decreased spatial distance between the distractor and the cue. A subsequent computational analysis revealed that the firing rate and not the neural mechanism per se, accounted for the positive correlation between mnemonic accuracy and spatial distance. Moreover, the computational modeling predicts an inverse correlation between accuracy and distractibility. In conclusion, any pharmacological modulation, pathological condition or memory training paradigm targeting the underlying neural circuitry and altering the net population firing rate during the delay is predicted to determine the amount of influence of a visual distraction.

Background: Plasmid encoded (CTX)-C-bla-M enzymes represent an important sub-group of class A beta-lactamases causing the ESBL phenotype which is increasingly found in Enterobacteriaceae including Klebsiella spp. Molecular typing of clinical ESBL-isolates has become more and more important for prevention of the dissemination of ESBL-producers among nosocomial environment.

Methods: Multiple displacement amplified DNA derived from 20 K. pneumoniae and 34 K. oxytoca clinical isolates with an ESBL-phenotype was used in a universal CTX-M PCR amplification assay. Identification and differentiation of (CTX)-C-bla-M and (OXY)-O-bla/K1 sequences was obtained by DNA sequencing of M13-sequence-tagged CTX-M PCR-amplicons using a M13-specific sequencing primer.

Results: Nine out of 20 K. pneumoniae clinical isolates had a (CTX)-C-bla-M genotype. Interestingly, we found that the universal degenerated primers also amplified the chromosomally located K1-gene in all 34 K. oxytoca clinical isolates. Molecular identification and differentiation between (CTX)-C-bla-M and (OXY)-O-bla/K1-genes could only been achieved by sequencing of the PCR-amplicons. In silico analysis revealed that the universal degenerated CTX-M primer-pair used here might also amplify the chromosomally located (OXY)-O-bla and K1-genes in Klebsiella spp. and K1-like genes in other Enterobacteriaceae.

Conclusion: The PCR-based molecular typing method described here enables a rapid and reliable molecular identification of (CTX)-C-bla-M, and (OXY)-O-bla/K1-genes. The principles used in this study could also be applied to any situation in which antimicrobial resistance genes would need to be sequenced.

We analyze two different feature selection problems: finding a minimal feature set optimal for classification (MINIMAL-OPTIMAL) vs. finding all features relevant to the target variable (ALL-RELEVANT). The latter problem is motivated by recent applications within bioinformatics, particularly gene expression analysis. For both problems, we identify classes of data distributions for which there exist consistent, polynomial-time algorithms. We also prove that ALL-RELEVANT is much harder than MINIMAL-OPTIMAL and propose two consistent, polynomial-time algorithms. We argue that the distribution classes considered are reasonable in many practical cases, so that our results simplify feature selection in a wide range of machine learning tasks.

In order to retain information in working memory (WM) during a delay, distracting stimuli must be ignored. This important ability improves during childhood, but the neural basis for this development is not known. We measured brain activity with functional magnetic resonance imaging in adults and 13-year-old children. Data were analyzed with an event-related design to isolate activity during cue, delay, distraction, and response selection. Adults were more accurate and less distractible than children. Activity in the middle frontal gyrus and intraparietal cortex was stronger in adults than in children during the delay, when information was maintained in WM. Distraction during the delay evoked activation in parietal and occipital cortices in both adults and children. However, distraction activated frontal cortex only in children. The larger frontal activation in response to distracters presented during the delay may explain why children are more susceptible to interfering stimuli.

This book brings together important topics of current research in probabilistic graphical modeling, learning from data and probabilistic inference. Coverage includes such topics as the characterization of conditional independence, the learning of graphical models with latent variables, and extensions to the influence diagram formalism as well as important application fields, such as the control of vehicles, bioinformatics and medicine.

We propose an algorithm for learning the Markov boundary of a random variable from data without having to learn a complete Bayesian network. The algorithm is correct under the faithfulness assumption, scalable and data efficient. The last two properties are important because we aim to apply the algorithm to identify the minimal set of random variables that is relevant for probabilistic classification in databases with many random variables but few instances. We report experiments with synthetic and real databases with 37, 441 and 139352 random variables showing that the algorithm performs satisfactorily.

We present a sound and complete graphical criterion for reading dependencies from the minimal undirected independence map G of a graphoid M that satisfies weak transitivity. Here, complete means that it is able to read all the dependencies in M that can be derived by applying the graphoid properties and weak transitivity to the dependencies used in the construction of G and the independencies obtained from G by vertex separation. We argue that assuming weak transitivity is not too restrictive. As an intermediate step in the derivation of the graphical criterion, we prove that for any undirected graph G there exists a strictly positive discrete probability distribution with the prescribed sample spaces that is faithful to G. We also report an algorithm that implements the graphical criterion and whose running time is considered to be at most O(n(2)(e + n)) for n nodes and e edges. Finally, we illustrate how the graphical criterion can be used within bioinformatics to identify biologically meaningful gene dependencies.

Combinatorial interactions among transcription factors are critical to directing tissue-specific gene expression. To build a global atlas of these combinations, we have screened for physical interactions among the majority of human and mouse DNA-binding transcription factors (TFs). The complete networks contain 762 human and 877 mouse interactions. Analysis of the networks reveals that highly connected TFs are broadly expressed across tissues, and that roughly half of the measured interactions are conserved between mouse and human. The data highlight the importance of TF combinations for determining cell fate, and they lead to the identification of a SMAD3/FLI1 complex expressed during development of immunity. The availability of large TF combinatorial networks in both human and mouse will provide many opportunities to study gene regulation, tissue differentiation, and mammalian evolution.

Recent models of the oculomotor delayed response task have been based on the assumption that working memory is stored as a persistent activity state (a 'bump' state). The delay activity is maintained by a finely tuned synaptic weight matrix producing a line attractor. Here we present an alternative hypothesis, that fast Hebbian synaptic plasticity is the mechanism underlying working memory. A computational model demonstrates a working memory function that is more resistant to distractors and network inhomogeneity compared to previous models, and that is also capable of storing multiple memories.

Integrative systems biology has emerged as an exciting research approach in molecular biology and functional genomics that involves the integration of genomics, proteomics, and metabolomics datasets. These endeavors establish a systematic paradigm by which to interrogate, model, and iteratively refine our knowledge of the regulatory events within a cell. Here we review the latest technologies available to collect high-throughput measurements of a cellular state as well as the most successful methods for the integration and interrogation of these measurements. In particular we will focus on methods available to infer transcription regulatory networks in mammals.